Channel pruning is one of the predominant approaches for deep model
compression. Existing pruning methods either train from scratch with sparsity
constraints on channels, or minimize the reconstruction error between the
pre-trained feature maps and the compressed ones. Both strategies suffer from
some limitations: the former kind is computationally expensive and difficult to
converge, whilst the latter kind optimizes the reconstruction error but ignores
the discriminative power of channels. To overcome these drawbacks, we
investigate a simple-yet-effective method, called discrimination-aware channel
pruning, to choose those channels that really contribute to discriminative
power. To this end, we introduce additional losses into the network to increase
the discriminative power of intermediate layers and then select the most
discriminative channels for each layer by considering the additional loss and
the reconstruction error. Last, we propose a greedy algorithm to conduct
channel selection and parameter optimization in an iterative way. Extensive
experiments demonstrate the effectiveness of our method. For example, on
ILSVRC-12, our pruned ResNet-50 with 30% reduction of channels even outperforms
the original model by 0.39% in top-1 accuracy.

介绍了一种有效的基于鉴别力的通道剪枝方法，利用额外的损失函数增加中间层的鉴别力并根据重构误差和额外损失函数选择最具鉴别力的通道，并提出了一种迭代贪心算法来优化参数和通道选择。对类似于 ILSVRC-12 的测试数据，使用该算法对 ResNet-50 剪枝显示出相当有效的性能。